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============================== AidTSP 2023 ==============================

The 2023 International Workshop on AI-Driven Trustworthy, Secure, and Privacy-Preserving Computing (AidTSP 2023)

Organized in conjunction with
IEEE INFOCOM 2023 - International Conference on Computer Communications, 17-20 May 2023, New York Area, USA

https://infocom2023.ieee-infocom.org/2023-international-workshop-ai-driven-trustworthy-secure-and-privacy-preserving-computing-aidtsp

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OVERVIEW
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Artificial intelligence (AI), together with its learning techniques, including machine learning (ML), and deep learning (DL), represents a significant evolution in computer science and data processing that is rapidly changing many industries and profoundly influencing people’s daily lives. AI-driven solutions have already found applications in next-generation computing, communication, and decision-making on the networking paradigms, namely, the Internet of Things (IoT), Cyber-Physical Systems (CPS), Fog/Edge computing, Cloud computing, etc. Such solutions are vastly used in high-stakes applications like industrial automation, healthcare, automotive, energy, business, government, education, and justice, moving us toward a more algorithmic society.

Despite so many greatest benefits, unsecured or unreliable computation, privacy-violating data processing, communication, and inaccurate, faulty, unethical, unfair, or biased decisions in these platforms sometimes directly or indirectly cause harm to the application’s performance, its users, and society. Moreover, an individual’s privacy is significantly threatened by AI-based cyberattacks. The rise of AI-enabled cyberattacks could cause an explosion of networking penetration, personal data theft, malicious traffic data, and epidemic spread of intelligent malware in IoT/CPS/Cloud/Edge/Fog paradigms. Furthermore, AI-driven technologies require a training and testing process, introducing additional problems in securing and protecting training data and algorithms. Many ML/DL models are vulnerable against well-designed adversarial input samples. Distributed sharing data, outsourcing data, and associated algorithms for training require integrity and trustworthiness in the training stage. Furthermore, end-user data privacy and learning models must be protected. Thus, trustworthy, security, and privacy-preserving (TSP) computing in IoT/CPS/Cloud with AI-driven approaches is becoming an issue.

To that end, this workshop aims to provide a forum for researchers, engineers, and professionals across academia, government, and industries, to exchange ideas, present early results, and provide future visions concerning AI-driven Trustworthy, Secure, and Privacy-Preserving Computing (AidTSP). Specific topics of interest include but are not limited to the following.

- AI-driven TSP computing
- AI-driven TSP computing in IoT/CPS/Edge/Fog/Cloud paradigms
- AI-driven TSP computing Big data paradigm
- AI-driven TSP computing with blockchain
- AI-driven TSP computing against AI-driven malware and fault injections
- AI-driven TSP computing against AI-driven supply chain & hardware attacks
- AI-driven TSP computing in big data capture, classification, and analytics
- AI-driven TSP computing in OS, virtualization, database, and software systems
- AI-driven detection, prevention, and recovery against potential threats
- AI-driven applied cryptography and security protocols
- AI-driven defense against AI-driven threats/attacks
- AI-driven data trust, system trust, service trust, and application trust
- TSP with learning methods (ML/DL/DRL/FL)
- TSP, anonymity, and resilience analysis on AI
- TSP with AI-driven data mining and knowledge discovery
- TSP concerns with AI-driven technologies, such as GAN
- Trustworthy learning methods (ML/DL/DRL/FL) for IoT/Edge/Fog/Cloud computing
- Trustworthy AutoML, hardware components or devices, and embedded things
- Trustworthiness verification for AI-driven authentication, access control, & monitoring
- Theoretical studies on big data system trustworthiness, privacy, and security
- Fairness, explainability, accountability, reliability, and safety with AI
- AI-driven TSP measures, metrics, verification, and validation

IMPORTANT DATES:
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Submission Deadline: January 12, 2023 [Extended Deadline]
Notification of Acceptance: February 6, 2023
Camera Ready: March 6, 2023
Workshop: May 20, 2023

SUBMISSION:
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Submission link: https://edas.info/N30343

The workshop accepts only novel, previously unpublished papers which are not under review by any journal or conference. Accepted and presented research papers and extended abstracts will be published in the IEEE INFOCOM 2023 Conference Proceedings and submitted to IEEE Xplore®.

Papers must be formatted in the standard IEEE two-column format (https://www.ieee.org/conferences/publishing/templates.html) that is used by the INFOCOM 2023 main conference. They must not exceed, including references, 6 pages in length for technical papers (with the option of 2 extra pages at a cost per extra page) and 2 pages in length for demo papers. All submitted papers in PDF format will go through a peer review process, and all accepted papers presented by one of the authors at the workshop will be published in the IEEE INFOCOM 2023 proceedings and IEEE Xplore. Please follow the submission link to submit your paper through EDAS:
https://edas.info/N30343



ORGANIZING COMMITTEE
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GENERAL CHAIRS:
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Jie Wu, Temple University, USA
Sy-Yen Kuo, National Taiwan University, Taiwan

TPC CHAIRS:
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Zakirul Alam Bhuiyan (Point of Contact), Fordham University, USA
Guojun Wang, Guangzhou University, China

TPC VICE CHAIRS:
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Houbing Song, University of Maryland, USA
Celimuge Wu, The University of Electro-Communications, Japan
Anna Kobusinska, Poznan University of Technology, Poland
Adnan Anwar, Deakin University, Australia
Changqing Luo, Virginia Commonwealth University, USA
Xiaokang Zhou, Shiga University, Japan
Peng Hao, Beihang University, China

PUBLICITY CHAIRS:
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Al-Sakib Khan Pathan, United International University, Bangladesh
Weizhi Meng, Technical University of Denmark, Denmark
Rafiul Hassan, University of Maine, USA

STEERING COMMITTEE:
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Zakirul Alam Bhuiyan, Fordham University, USA (Chair)
Jie Wu, Temple University, USA (Chair)
Sy-Yen Kuo, National Taiwan University, Taiwan
Vincenzo Piuri, University of Milan, Italy
Frank Hsu, Fordham University, USA
Laurence T. Yang, St Francis Xavier University, Canada
Jiannong Cao, The Hong Kong Polytechnic University, Hong Kong
Kuan-Ching Li, Providence University, Taiwan
Mohammed Atiquzzaman, Univ. of Oklahoma, USA
Raymond Choo, The Univ. of Texas at San Antonio, USA
Giancarlo Fortino, University of Calabria, Italy
Aniello Castiglione, Univ. of Naples Parthenope, Italy


TECHNICAL PROGRAM COMMITTEE MEMBERS:
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Please see: https://infocom2023.ieee-infocom.org/2023-international-workshop-ai-driven-trustworthy-secure-and-privacy-preserving-computing-aidtsp-1

====================
Questions can be directed to AidTSPresearch AT gmail DOT com

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Regards,
Sakib
Publicity Chair

--
Al-Sakib Khan Pathan, Ph.D., SMIEEE
Editor-in-Chief: International Journal of Computers and Applications, Taylor & Francis, UK
Editor-in-Chief: Journal of Cyber Security Technology, Taylor & Francis, UK
Associate Editor: Connection Science, Taylor & Francis, UK ; IJCSE, Inderscience
Editor: AHSWN, Old City Publishing, USA ; IJSNet, Inderscience ; MJCS
All My Books: https://sites.google.com/site/spathansite/books

Professor, Department of Computer Science and Engineering
United International University (UIU), Dhaka, Bangladesh
Email: sakib.pathan@gmail.com, spathan@ieee.org
URL: https://sites.google.com/site/spathansite/